Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30037
Support quantization for modules with reused submodules, e.g. relu (automatically make unique)
We first do a pass on the graph to find all duplicate uses of the same module, and record the `Value`s of the
module instance, for each of these values we create a new module and change the access to that module.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D18821483
fbshipit-source-id: 1698b981e9e9f0c728d9f03fcbcfbd260151f679
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30548
ClassTypes can be shared among different module instances, but previously we assumed
they would be unique, this PR enables the insert_observers pass to work with shared class types
Test Plan:
python test/test_jit.py
python test/test_quantization.py
Imported from OSS
Differential Revision: D18802465
fbshipit-source-id: b782e71e44a043af45577ac2b5c83e695155bb8b
Summary:
This fixes the second issue reported in https://github.com/pytorch/pytorch/issues/29909 namely, a loop counter is assigned the wrong values after transitioning to a bailout graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30186
Differential Revision: D18646845
Pulled By: Krovatkin
fbshipit-source-id: 1f7c601dd9f35892979385ffa132fb0886a4f203
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30362
Right now the qat modules(qat.ConvBn2d, qat.ConvBnReLU2d, qat.Conv2d)
are not convinent to support other dimensions of Conv, this PR refactors
these modules so that we can support Conv1d/Conv3d better
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D18691152
fbshipit-source-id: 5b561e6b054eadd31b98cabdf1ac67a61ee9b805
Summary:
In this PR, we mainly handle the case there are multiple usage of a Value when inserting the quant-dequant pair. This change will add one dequant for each usage of the Value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30145
Differential Revision: D18671600
Pulled By: lly-zero-one
fbshipit-source-id: 61324a98861da85b80dcf7e930381311118ae53b
Summary:
This PR looks for a `constants.pkl` file at the top level in a zip file
in `torch.load`. If found, it calls `torch.jit.load` instead and issues
a warning to call `torch.jit.load` directly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29339
Differential Revision: D18611095
Pulled By: driazati
fbshipit-source-id: f070a02f6b5509054fc3876b3e8356bbbcc183e1
Summary:
A prim::BailOut also needs to capture max trip counts as for some graphs they aren't constants and they are used in continuation graphs to figure out the remaining number of iterations to run.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30097
Differential Revision: D18624446
Pulled By: Krovatkin
fbshipit-source-id: 085d25981c6669f65848996cd2d50066cc252048
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29577
`torch.autograd.grad` can return none is one of the input is not in the
autograd graph or not requires_grad, this fix it so that it return a
list of optional tensor instead of list of tensor.
This might have BC issue unfortunately, but I think it's rare both
internal and external (only training use it, and most of the training
use backward, instead of autograd.grad), so whitelist it.
Test Plan: Imported from OSS
Differential Revision: D18491642
fbshipit-source-id: d32b2b3446cf9e8b9a98f6d203a21a75643d8991
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29494
`calculate_qparams` of per channel quantization should return the axis, this
PR added this and also added corresponding support in graph mode
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D18580905
fbshipit-source-id: f9691c1f043f8bca39f81716a4d0b10f60a65396
Summary:
This uses newly added InlinedCallStack to print the original call stack
even if the real call stack is shallower because of inlining.
This change also makes torchscript stacktraces look like python ones.
Example:
```
torch.jit.script
def baz(c, b):
return c + b
torch.jit.script
def foo(c, b):
return baz(c, b)
torch.jit.script
def bar(c, b):
return foo(c, b)
bar(torch.rand(10), torch.rand(9))
```
Output before:
```
Traceback (most recent call last):
File "fail.py", line 25, in <module>
bar(torch.rand(10), torch.rand(9))
RuntimeError: The size of tensor a (10) must match the size of tensor b (9) at non-singleton dimension 0
The above operation failed in interpreter, with the following stack trace:
at fail.py:15:11
torch.jit.script
def baz(c, b):
return c + b
~~~~~ <--- HERE
```
Output after:
```
Traceback (most recent call last):
File "fail.py", line 41, in <module>
bar(torch.rand(10), torch.rand(9))
RuntimeError: The size of tensor a (10) must match the size of tensor b (9) at non-singleton dimension 0
The above operation failed in interpreter.
Traceback (most recent call last):
File "fail.py", line 33
torch.jit.script
def bar(c, b):
return foo(c, b)
~~~ <--- HERE
File "fail.py", line 29, in foo
torch.jit.script
def foo(c, b):
return baz(c, b)
~~~ <--- HERE
File "fail.py", line 25, in baz
torch.jit.script
def baz(c, b):
return c + b
~~~~~ <--- HERE
```
Output of non-scripted python code:
```
Traceback (most recent call last):
File "fail.py", line 36, in <module>
bar(torch.rand(10), torch.rand(9))
File "fail.py", line 21, in bar
return foo(c, b)
File "fail.py", line 18, in foo
return baz(c, b)
File "fail.py", line 15, in baz
return c + b
RuntimeError: The size of tensor a (10) must match the size of tensor b (9) at non-singleton dimension 0
```
Differential Revision: D18532812
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: e7e5ba5e4a8f1c7086406271d0f1685d9db8541a
Summary:
Stacked PRs
* https://github.com/pytorch/pytorch/issues/29244 - Use custom CRC
* **https://github.com/pytorch/pytorch/issues/29232 - Add zipfile serialization**
This adds a serialization method that uses a zipfile (https://github.com/pytorch/pytorch/issues/26567). Right now it is
guarded behind a flag `_use_new_zipfile_serialization`. In release mode it seems to have performance about the same / slightly better than the current serialization in some simple benchmarks for large/small tensors.
Follow ups:
* Flip the `_use_new_zipfile_serialization` flag
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29232
Differential Revision: D18332036
Pulled By: driazati
fbshipit-source-id: 1bac0847c4d599612cba905f2cac8248783be2f4
Summary:
Fix for https://github.com/pytorch/pytorch/issues/21545
We we were silently giving wrong semantics previously:
Python behavior:
```
def test(x=[]):
x.append(1)
return len(x)
print(test()) # 1
print(test()) # 2
```
By checking at the python layer, we prevent any new models from serializing this behavior but do not break existing serialized models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29833
Differential Revision: D18513168
Pulled By: eellison
fbshipit-source-id: 6fe73f28e1f9d39dedeaf67a04718089d14401a1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28988
Make ModuleList, Sequential, ModuleDict go through the same pathway as other modules, cleaning up a bunch of code and allowing them to define custom forwards and other methods.
EDIT: Previously, we would ignore an nn.Sequential attribute if it was not in `__constants__` ("did you forget to add it to Constants"). This PR scripts it even if it is not in `__constants__`. Is that what we want?
Test Plan: Imported from OSS
Differential Revision: D18402821
Pulled By: eellison
fbshipit-source-id: dd4f28fb0df0d1ba4ad1b3bc34ba141959a433f7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29529
Pull Request resolved: https://github.com/pytorch/glow/pull/3771
We would like to replace `conv_prepack` with `conv2d_prepack` and `conv_unpack` with `conv2d_unpack`.
This makes the naming consistent between 2D and 3D conv:
```
torch.ops.quantized.conv2d_prepack
torch.ops.quantized.conv2d_unpack
torch.ops.quantized.conv2d
torch.ops.quantized.conv3d_prepack
torch.ops.quantized.conv3d_unpack
torch.ops.quantized.conv3d
```
We should do this earlier rather than later when we have more users for the quantized conv2d ops, for better engineering.
The replacement bash command is as the follows:
```
find ./ -type f -exec sed -i -e 's/quantized::conv_prepack/quantized::conv2d_prepack/g' {} \;
find ./ -type f -exec sed -i -e 's/quantized::conv_unpack/quantized::conv2d_unpack/g' {} \;
find ./ -type f -exec sed -i -e 's/torch.ops.quantized.conv_prepack/torch.ops.quantized.conv2d_prepack/g' {} \;
find ./ -type f -exec sed -i -e 's/torch.ops.quantized.conv_unpack/torch.ops.quantized.conv2d_unpack/g' {} \;
```
ghstack-source-id: 93661879
Test Plan: CI
Reviewed By: jackm321
Differential Revision: D18421079
fbshipit-source-id: 17ae8b1ee79223bd2c5d4bbccd57af6580c4ab12
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28985
Remove the observer module in the quantized model
Test Plan:
python test/test_jit.py 'TestJit.test_insert_quant_dequant'
Imported from OSS
Differential Revision: D18253777
fbshipit-source-id: 26081c4c3fd3dc049cafa8c0383219bc4c233589
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29432
This removes a lot of the private methods on torch._C.ScriptModule,
and instead implements functionality in terms of slot_dict_impl views
to implement _parameter, _buffers, and _modules in nn.Module.
A followup PR should also remove the _register_attribute,
_register_module, and _register_parameter methods, but this requires
more refactoring of the way tracing creates modules and replication
for data parallel works.
Test Plan: Imported from OSS
Differential Revision: D18387963
Pulled By: zdevito
fbshipit-source-id: f10d47afeb30c1e05d704ae5ac4166830933125c
Summary:
Fixes https://github.com/pytorch/pytorch/issues/17662
I'm not sure if `arange` needs to be in python_arg_parser at all, given the schemas in native_functions.yaml. In any case this at least fixes the dytpe mismatch.
In follow up PRs I will try to handle some of the other ops that do type inference at the python level, like randint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27629
Differential Revision: D17885939
Pulled By: eellison
fbshipit-source-id: f97a8bc722b7ab77de1c42a992e49a4a3175ad60
Summary:
For the same reason we don't allow iteration over heterogenous types (modulelists/tuples) with types that don't have a static length, we also can't break/continue within them - we need to statically know all types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29474
Differential Revision: D18406097
Pulled By: eellison
fbshipit-source-id: 70ed3fc4947b6237cdd6703135a988a5c13ce786
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29332
Even though we're statically typed, this can be useful, e.g. as
shorthand when iterating through a module list.
Test Plan: Imported from OSS
Differential Revision: D18393097
Pulled By: suo
fbshipit-source-id: aa42e955f88d1b8a876d0727055eb596453b9839
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29269
Hit this bug when I have an attribute of type `Optional[Tensor]` which
is initialized to None and reassigned later to some tensor.
Test Plan:
.
Imported from OSS
Differential Revision: D18364338
fbshipit-source-id: d8e1277a84ab7d80331cba83f5639469d398632e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28828
This updates torch::script::Module to more closely match the behavior
of nn.Module. In particular, it implements the (optionally recurisive)
iterators that retrieve submodules, parameters, and buffers and makes
their names match the python versions.
This also removes the individual accessors for Parameter, Module, Buffer, etc.
and replaces them with a single `attr` function which is equivalent to
writing `a.foo` in Python (`setattr` emulates `a.foo = v`).
As we build out the user-facing API for TorchScript values this will end
up matching how an attribute is accessed on general objects.
This PR preservers the python bindings for script::Module by emulating the
old API at the binding level. A followup will clean up the usage to more
directly match the C++ API.
Test Plan: Imported from OSS
Differential Revision: D18197611
Pulled By: zdevito
fbshipit-source-id: 7ee4dcbb258605d1c988314b05d938423f1ccee5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29249
This splits out all the tests that are "easy", leaving `TestJit`,
`TestScript`, the autogenerated tests, and a small docs test.
Splitting those into reasonable chunks is more effort which is less
mechanical.
Differential Revision: D18339007
Test Plan: Imported from OSS
Pulled By: suo
fbshipit-source-id: 69164b9f9a2c379fe8923a846c98dd3c37ccb70e
Summary:
Type objects in python have an attribute `__abstractmethods__` that throws when it is accessed, so we were failing with an AttributeError whenever a type was used in TorchScript.
This pr prevents that error from happening. We can't just throw when a type is used because it could be used to access a static method: https://github.com/pytorch/pytorch/pull/27163
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28053
Differential Revision: D18332347
Pulled By: eellison
fbshipit-source-id: 9c7f2220f92674ad4d903621d9762cecc566ab0d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28620
All Tensors are Variables now, they just happen to have requires_grad=False. Tensors ALWAYS have `VariableTensorId` in their type set.
When constructing this patch, I had to make decisions about what I would fix in this patch, and what I would leave for follow up PRs. Here is the cleanup that happens in this patch:
- The `is_variable` property is removed from TensorOptions. I removed this immediately because unlike Tensor::is_variable, TensorOptions::is_variable doesn't respect our VariableTensorId thread-local state. This means that there were a bunch of places where TensorOptions::is_variable was false, which is obviously bogus in the world when tensor and variable are merged. Instead of keeping the method as a function that always returns true, I just opted to remove it entirely (it's not public API.) All places we set `is_variable` are deleted.
- Knock on effect: there is no longer a separate DeprecatedTypeProperties for the variable and non-variable versions of type.
- Knock on effect: instead of asserting on TensorOptions::is_variable, instead we just test `at::impl::variable_is_excluded()`
- There is now only one copy of the cuDNN RNN dropout cache, not two (I'm not sure why we had two to begin with)
Some cleanup that doesn't happen in this patch:
- Eliminating unnecessary uses of `make_variable`
- Eliminating `Tensor::is_variable`
The most subtle part of this patch is retaining tracing behavior: the fact that everything is a Variable means that more code gets routed to VariableType than before; this can change traces. I identified two places where we didn't appropriately turn off VariableType, mostly factory functions:
- `torch.tensor` must turn off VariableType before invoking `at::empty` to construct the tensor, as it subsequently does direct data access
- `tensor_slow` (invoked when you pass a Python scalar to a tensor argument) must turn off VariableType before calling `scalar_to_tensor` so the scalar gets traced as constant, rather than as a call to `scalar_to_tensor`.
Honestly, these are all giant hacks, and should be replaced with a more specialized guard that just toggles tracing.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: dreiss
Differential Revision: D18171156
Pulled By: ezyang
fbshipit-source-id: 5b6a045beba37492647e350190f495114e86504d
